arXiv subjects

Taner Cokyasar

Publications and source records attributed to Taner Cokyasar.

At least 19 recordsLinked to original sources

arXiv2026

Modeling of Mobility and Energy Policies in an Agent-Based Framework: Case Studies for Chicago Region in 2050

Metropolitan regions are simultaneously pursuing several interventions to improve mobility, accessibility, and energy efficiency, necessitating integrated tools to understand how these policies interact to affect travel behavior, energy use, and infrastructure needs. This paper evaluates the combined impacts of electrification, freight demand management, road pricing, parking reform, and transit expansion on the Chicago metropolitan transportation system in 2050, using a business-as-usual (BAU) scenario as the baseline. We employ POLARIS, a large-scale agent-based modeling framework calibrated to 2019 conditions, to simulate nine policy scenarios for the seven-county northeastern Illinois region. The framework co-simulates activity-based passenger demand, endogenous freight generation, multimodal traffic assignment, and transit operations, with charging infrastructure and freight operations optimized for each case. Our findings reveal that under the high electrification scenario, total fuel mass declines by 68% while total charging energy increases by approximately 4-8x from BAU, resulting in a peak power demand near 4 GW concentrated in the urban core. Furthermore, freight management policies reduce freight VMT by increasing trip frequency but shortening distances, smart road pricing most effectively reduces auto VMT, and transit expansion boosts ridership by 18% relative to BAU. By presenting the first integrated, agent-based scenario framework for Chicago that jointly evaluates these interventions, this study provides actionable insights for regional transportation planning, grid infrastructure investment, and emissions reduction, highlighting the value of targeted charger upgrades and coordinated policy bundles.

physics.soc-ph↗

arXiv2026

Bus Fleet Electrification Under Capital Cost and Scheduling Constraints: A Five-Agency Case Study

As transit agencies consider bus fleet electrification, understanding the efficiency and cost of replacing diesel buses (DBs) with battery electric buses (BEBs) is critical. To evaluate this, this study applies a mixed-fleet optimization model, integrating scheduling, charging, and fleet composition decisions, across five agencies: Santa Monica's Big Blue Bus (BBB), the Chicago Transit Authority (CTA), Knoxville Area Transit (KAT), the Metropolitan Atlanta Rapid Transit Authority (MARTA), and Manhattan's Metropolitan Transportation Authority (MTA) bus service. By calculating electric fleet share, the BEB/DB replacement ratio, transit-link density, and vehicle activity-time allocation, the study finds that while optimized fleets remain majority-electric, vehicle substitution is rarely one-to-one. Average replacement ratios range from 1.101 for CTA to 1.245 for KAT, with higher transit-link density networks like CTA and MTA requiring fewer replacement buses per diesel bus displaced than lower-density networks like MARTA and KAT. While these relationships are descriptive rather than causal, non-revenue vehicle activity may help explain the differences. By shifting the focus from simple electric fleet share to diesel replacement efficiency, this multi-agency comparison demonstrates that transit agencies should use the replacement ratio to accurately forecast additional fleet capacity requirements and avoid the costly assumption of strict one-to-one vehicle substitution.

math.OC↗

arXiv2026

Intercity EV Charging Readiness in T\"urkiye: A Countrywide Scheduling and Economic Assessment

This paper assesses the readiness of T\"urkiye's electric vehicle (EV) charging infrastructure to support cost-competitive intercity travel compared to internal combustion engine (ICE) vehicles. Utilizing a directed acyclic graph-based scheduling framework, charging stops are optimized for over 2 million synthetic intercity EV trips across the national highway network, evaluating 9,641 licensed charging stations. An eight-scenario case study examines the interplay between charger power (5--350 kW) and station capacity (5 and 50 plugs), supplemented by a 200-run parametric sensitivity analysis on EV range, discharge rate, charger power, and state-of-charge (SoC) operating bounds. A three-tier economic analysis isolates operational costs, total cost of ownership (TCO), and infrastructure investments. Economically, EVs demonstrate a 23--26% operational cost advantage driven by T\"urkiye's favorable fuel-to-electricity price ratio, maintaining a 10--12% TCO advantage despite higher purchase prices. However, full-system infrastructure savings heavily depend on station sizing, yielding positive returns at 5 plugs per station but negative returns at 50. Ultimately, vehicle discharge rate emerges as the dominant economic lever. This study provides the first countrywide, vehicle-level assessment of intercity EV charging readiness for an emerging market, offering actionable insights for policymakers on infrastructure gaps, station sizing tradeoffs, and adoption incentives.

math.OC↗

arXiv2026

A National-Scale EV Charging Scheduling Framework: Optimal Detour Routing Under Infrastructure Capacity Constraints

As electric vehicle (EV) adoption grows, quantifying the scheduling burden and economic cost of long-distance travel under the existing charging infrastructure becomes increasingly important for infrastructure planning and policy. This paper presents a scalable, optimization-based framework for scheduling EV charging stops along real-world charging stations and simulated long-distance personal vehicle trajectories across the United States using POLARIS. Taking the existing charging network as fixed input, the framework minimizes total detour and queuing costs for each vehicle while respecting plug capacity constraints at each station. The methodology proceeds in three phases: (i) infeasibility pruning via a forward-pass reachability heuristic, (ii) per-vehicle optimal charging schedule computation via dynamic programming on a directed acyclic graph, and (iii) capacity-aware iterative congestion resolution through a penalty-based heuristic that augments detour costs at congested stations, with a first-in, first-out queue fallback. Applied to approximately 2.7M origin--destination vehicle trajectories derived from a 1\% sample of national personal travel demand within the POLARIS agent-based transportation simulation framework and covering 14,260 DC fast charging stations with 68,641 plugs from the Alternative Fuels Station Locator, the framework produces capacity-feasible schedules in under 1.3 hours on a 128-core high-performance computing cluster without requiring any commercial optimization solver. A three-tier economic analysis spanning operational costs, total cost of ownership, and amortized infrastructure investment is conducted to evaluate EV cost competitiveness relative to internal combustion engine vehicles across scenarios.

math.OC↗

arXiv2026

Modeling and Calibration of Supplier Selection Problem in Freight Agent-Based Simulations

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for international shipments. Our approach integrates trade relationships between industry sectors, transportation costs, and supplier rating model adapted from existing literature. The model's core objective is to minimize the discrepancy between modeled and observed commodity flows while ensuring a close match to regional shipping distance distributions. Implemented and tested across four major U.S. metropolitan areas, Atlanta, Chicago, Dallas-Fort Worth, and Los Angeles, the model demonstrates high fidelity in replicating observed freight patterns. Key findings reveal consistent alignment with national shipping distance trends and highlight significant spatial variations in commodity trade assignments and demand across the study regions. This behaviorally informed and transport-sensitive framework is designed to approximate real-world decision-making, providing a robust tool for policymakers and planners to evaluate targeted interventions, assess infrastructure investments, and enhance supply chain resilience in the face of disruptions.

math.OC↗

arXiv2026

Integrated Optimization of Scheduling and Flexible Charging in Mixed Electric-Diesel Urban Transit Bus Systems

The transition of transit fleets to alternative powertrains offers a potential pathway to reducing the cost of mobility. However, the limited range and long charging durations of battery electric buses (BEBs) introduce significant operational complexities, necessitating innovative scheduling and charging strategies. This study proposes an integrated mixed-integer linear programming model to optimize vehicle scheduling and charging strategies for mixed fleets of BEBs and diesel buses. Unlike existing models, which often assume a fixed BEB fleet size or restrict charging to a single charger type, our approach simultaneously determines the optimal fleet composition, scheduling, and flexible partial charging strategy incorporating both slow and fast chargers at garages and terminal stations. The model minimizes combined fleet purchase and operational costs. A queuing strategy is introduced, departing from traditional first-come, first-served methods by dynamically allocating waiting and charging times based on operational priorities and resource availability, improving overall scheduling efficiency. To overcome computational complexities arising from numerous variables, a column generation framework is developed, facilitating scalable solutions for large-scale transit networks. Numerical experiments using real-world transit data from the Chicago Transit Authority and the Pace suburban bus systems demonstrate the model's effectiveness. Results indicate that while a full transition to alternative powertrains results in a modest cost increase, optimal mixed-fleet configurations can actually reduce total system costs. Furthermore, sensitivity analyses reveal that restricting charging to garages significantly increases fleet size and operational costs, underscoring the potential of distributed opportunistic charging.

math.OC↗

arXiv2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem's non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area's multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

eess.SY↗

arXiv2026

Joint Optimization of Pattern, Headway, and Fleet Size of Multiple Urban Transit Lines with Perceived Headway Consideration and Passenger Flow Allocation

This study addresses the urban transit pattern design problem, optimizing stop sequences, headways, and fleet sizes across multiple routes and periods simultaneously to minimize user costs (composed of riding, waiting, and transfer times) under operational constraints (e.g., vehicle capacity and fleet size). A destination-labeled multi-commodity network flow (MCNF) formulation is developed to solve the problem at a large scale more efficiently compared to the previous literature. The model allows for flexible pattern options without relying on pre-defined candidate sets and simultaneously considers multiple operational strategies such as express/local services, short-turning, and deadheading. It evaluates perceived headways of joint patterns for passengers, assigns passenger flows to each pattern accordingly, and allows transfers across patterns in different directions. The mixed-integer linear programming (MILP) model is demonstrated with a city-sized network of metro lines in Chicago, IL, USA, achieving near-optimal solutions in hours. The total weighted journey times are reduced by 0.61% and 5.76% under single-route and multi-period multi-route scenarios respectively. The model provides transit agencies with an efficient tool for comprehensive service design and resource allocation, improving service quality and resource utilization without additional operational costs.

eess.SY↗

arXiv2026

Extreme-Scale EV Charging Infrastructure Planning for Last-Mile Delivery Using High-Performance Parallel Computing

This paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

math.OC↗

arXiv2025

Equity Impacts of Public Transit Network Redesign with Shared Autonomous Mobility Services

This study examines the equity impacts of integrating shared autonomous mobility services (SAMS) into transit system redesign. Using the Greater Chicago area as a case study, we compare two optimization objectives in multimodal transit network redesign: minimizing total generalized costs (equity-agnostic) versus prioritizing service in low-income areas (equity-focused). We evaluate the achieved accessibility of clustered zones with redesigned transit networks under two objectives, compared to driving and the existing transit network. The transit access gaps across zones and between transit and driving are found to be generally reduced with the introduction of SAMS, but less so with the subsequent improved infrastructure under budget. Differential improvement in equity is seen across suburbs and areas of the city, reflecting the disparity in current transit access and improvement potential. In particular, SAMS bridges the transit access gaps in suburban and city areas currently underserved by transit. The City of Chicago, which is also disproportionately home to vulnerable populations, offers an avenue to improve vertical equity. These findings demonstrate that SAMS can enhance both horizontal and vertical equity in transit systems, particularly when equity is explicitly incorporated into the design objective.

eess.SY↗

arXiv2024

Problem of Locating and Allocating Charging Equipment for Battery Electric Buses under Stochastic Charging Demand

Bus electrification plays a crucial role in advancing urban transportation sustainability. Battery Electric Buses (BEBs), however, often need recharging, making the Problem of Locating and Allocating Charging Equipment for BEBs (PLACE-BEB) essential for efficient operations. This study proposes an optimization framework to solve the PLACE-BEB by determining the optimal placement of charger types at potential locations under the stochastic charging demand. Leveraging the existing stochastic location literature, we develop a Mixed-Integer Non-Linear Program (MINLP) to model the problem. To solve this problem, we develop an exact solution method that minimizes the costs related to building charging stations, charger allocation, travel to stations, and average queueing and charging times. Queueing dynamics are modeled using an M/M/s queue, with the number of servers at each location treated as a decision variable. To improve scalability, we implement a Simulated Annealing (SA) and a Genetic Algorithm (GA) allowing for efficient solutions to large-scale problems. The computational performance of the methods was thoroughly evaluated, revealing that SA was effective for small-scale problems, while GA outperformed others for large-scale instances. A case study comparing garage-only, other-only, and mixed scenarios, along with joint deployment, highlighted the cost benefits of a collaborative and a comprehensive approach. Sensitivity analyses showed that the waiting time is a key factor to consider in the decision-making.

math.OC↗

arXiv2024

Impact of Transit on Mobility, Equity, and Economy in the Chicago Metropolitan Region

Transit is essential for urban transportation and achieving net-zero targets. In urban areas like the Chicago Metropolitan Region, transit enhances mobility and connects people, fostering a dynamic economy. To quantify the mobility and selected economic impacts of transit, we use a novel agent-based simulation model POLARIS to compare baseline service against a scenario in which transit is completely removed. The transit-removal scenario assumes higher car ownership and results in higher traffic congestion, numerous activity cancellations, and economic decline. In this scenario, average travel times increase by 14.2% regionally and 34.7% within the City of Chicago. The resulting congestion causes significant activity cancellations despite increased car ownership: 11.8% of non-work and 2.8% of work/school activities regionally, totaling an 8.6% overall cancellation rate. In the city, non-work cancellations would reach 26.9%, and work/school cancellations 7.3%, leading to a 19.9% overall cancellation rate. The impact varies between groups. Women and lower-income individuals are more likely to cancel activities than men and higher-income groups. Women account for 53.7% of non-work and 53.0% of total cancellations. The lowest 40% income group experiences 50.2% of non-work and 48.0% of overall cancellations. Combined, activity cancellations, travel time losses, and increased car ownership cost the region $35.4 billion. With annual public transit funding at $2.7 billion, the ratio is 13 to 1, underscoring transit's critical role in mobility, equity, and economic health.

math.OC↗

arXiv2024

Modeling Transit in a Fully Integrated Agent-Based Framework: Methodology and Large-Scale Application

This study presents a transit routing, assignment, and simulation framework which is fully embedded in a multimodal, multi-agent transportation demand and supply modeling platform. POLARIS, a high-performance agent-based simulation platform, efficiently integrates advanced travel and freight demand modeling, dynamic traffic and transit assignment, and multimodal transportation simulation within a unified framework. We focus on POLARIS's transit routing, assignment, and simulation components, detailing its structural design and essential terminologies. We demonstrate how the model integrates upstream decision-making processes - activity generation, location and timing choices, and mode selection, particularly for transit-inclusive trips - followed by routing, assignment decisions, and the movement of travelers and vehicles within a multimodal network. This integration enables modeling of interactions among all agents, including travelers, vehicles, and transportation service providers. The study reviews literature on transportation system modeling tools, describes the transit modeling framework within POLARIS, and presents findings from large-scale analyses of various policy interventions. Results from numerical experiments reveal that measures such as congestion pricing, transit service improvements, first-mile-last-mile subsidies, increased e-commerce deliveries, and vehicle electrification significantly impact transit ridership, with some interactions between these levers exhibiting synergistic or canceling effects. The case study underscores the necessity of integrating transit modeling within a broader multimodal network simulation and decision-making context.

eess.SY↗

arXiv2024

Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services

This study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.

eess.SY↗

arXiv2024

Electric Vehicle Supply Equipment Location and Capacity Allocation for Fixed-Route Networks

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger equipment costs were found to be the most significant component in the objective function, leading to a substantial reduction in cost when decreased. VOT costs exhibited a significant decrease with rising energy costs. An increase in VOT resulted in a notable rise in the number of fast chargers. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible.

math.OC↗

arXiv2023

Large-Scale Dynamic Ridesharing with Iterative Assignment

Transportation network companies (TNCs) have become a highly utilized transportation mode over the past years. At their emergence, TNCs were serving ride requests one by one. However, the economic and environmental benefits of ridesharing encourages them to dynamically pool multiple ride requests to enable people to share vehicles. In a dynamic ridesharing (DRS) system, a fleet operator seeks to minimize the overall travel cost while a rider desires to experience a faster (and cheaper) service. While the DRS may provide relatively cheaper trips by pooling requests, the service speed is contingent on the objective of the vehicle-to-rider assignments. Moreover, the operator must quickly assign a vehicle to requests to prevent customer loss. In this study we develop an iterative assignment (IA) algorithm with a balanced objective to conduct assignments quickly. A greedy algorithm from the literature is also tailored to further reduce the computational time. The IA was used to measure the impact on service quality of fleet size; assignment frequency; the weight control parameter of the two objectives on vehicle occupancy -- rider wait time and vehicle hours traveled. A case study in Austin, TX, reveals that the key performance metrics are the most sensitive to the weight parameter in the objective function.

math.OC↗

arXiv2023

Heuristic Solutions to the Single Depot Electric Vehicle Scheduling Problem with Next Day Operability Constraints

This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. A case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle.

math.OC↗

arXiv2022

A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications such as the e-commerce parcel delivery truck routing, however, mid-route recharging may not be necessary because of constraints on vehicle capacities and maximum allowed time for delivery. In this study, we develop a mixed-integer optimization model that exactly solves such a time-constrained capacitated vehicle routing problem, especially of interest to e-commerce parcel delivery vehicles. We compare our solution method with an existing metaheuristic and carry out exhaustive case studies considering four U.S. cities -- Austin, TX; Bloomington, IL; Chicago, IL; and Detroit, MI -- and two vehicle types: conventional vehicles and battery electric vehicles (BEVs). In these studies we examine the impact of vehicle capacity, maximum allowed travel time, service time (dwelling time to physically deliver the parcel), and BEV range on system-level performance metrics including vehicle miles traveled (VMT). We find that the service time followed by the vehicle capacity plays a key role in the performance of our approach. We assume an 80-mile BEV range as a baseline without mid-route recharging. Our results show that BEV range has a minimal impact on performance metrics because the VMT per vehicle averages around 72 miles. In a case study for shared-economy parcel deliveries, we observe that VMT could be reduced by 38.8\% in Austin if service providers were to operate their distribution centers jointly.

math.OC↗